Publication number  US8027467 B2 
Publication type  Grant 
Application number  US 12/370,463 
Publication date  Sep 27, 2011 
Filing date  Feb 12, 2009 
Priority date  Dec 4, 2002 
Fee status  Paid 
Also published as  US7555122, US20040114756, US20090147948 
Publication number  12370463, 370463, US 8027467 B2, US 8027467B2, USB28027467, US8027467 B2, US8027467B2 
Inventors  Bodo Möller, Tsuyoshi Takagi 
Original Assignee  Wired Connections Llc 
Export Citation  BiBTeX, EndNote, RefMan 
Patent Citations (20), NonPatent Citations (41), Classifications (7), Legal Events (4)  
External Links: USPTO, USPTO Assignment, Espacenet  
This application is a continuation of U.S. patent application Ser. No. 10/310,735 filed Dec. 4, 2002 which is herein incorporated by reference in its entirety.
The invention describes an elliptic curve point multiplication method with resistance against sidechannel attacks, which are a big threat for use in cryptography, e.g. for key exchange, encryption, or for digital signatures.
Implementations of elliptic curve cryptosystems may be vulnerable to sidechannel attacks ([1], [2]) where adversaries can use power consumption measurements or similar observations to derive information on secret scalars e in point multiplications eP.
One distinguishes between differential sidechannel attacks, which require correlated measurements from multiple point multiplications, and simple sidechannel attacks, which directly interpret data obtained during a single point multiplication. Randomization can be used as a countermeasure against differential sidechannel attacks.
In particular, for elliptic curve cryptography, projective randomization is a simple and effective tool ([3]):
If (X, Y, Z) represents the point whose affine coordinates are (X/Z^{2}, Y/Z.^{3}) another representation of the same point that cannot be predicted by the adversary is obtained by substituting (r^{2}X, r^{3}Y, rZ) with a randomly chosen secret nonzero field element r. (When starting from an affine representation (X,Y), this simplifies to (r^{2}X, r^{3}Y, r).)
Simple sidechannel attacks can be easily performed because usually the attacker can tell apart point doublings from general point additions.
Thus point multiplication should be implemented using a fixed sequence of point operations that does not depend on the particular scalar.
Note that it is reasonable to assume that point addition and point subtraction are uniform to the attacker as point inversion is nearly immediate (dummy inversions can be inserted to obtain the same sequence of operations for point additions as for point subtractions).
Various point multiplication methods have been proposed that use an alternating sequence of doublings and additions:
The simplest approach uses a binary point multiplication method with dummy additions inserted to avoid dependencies on scalar bits ([3]); however as noted in [4] it may be easy for adversaries to determine which additions are dummy operations, so it is not clear that this method provides sufficient security. For odd scalars, a variant of binary point multiplication can be used where the scalar is represented in balanced binary representation (digits −1 and +1) ([5]). Also Montgomery's binary point multiplication method ([6]), which maintains an invariant Q_{1}−Q_{o}=P while computing eP using two variables Q_{o}, Q_{1}, can be adapted for implementing point multiplication with a fixed sequence of point operations ([7], [8], [9], [10], [11]).
With this approach, specific techniques can be used to speed up point arithmetic:
The doubling and addition steps can be combined; ycoordinates of points may be omitted during the computation ([6], [9], [10], [11]); and on suitable hardware, parallel execution can be conveniently used for improved efficiency ([10], [11]).
All of the above point multiplication methods are binary. Given sufficient memory, efficiency can be improved by using 2^{w}ary point multiplication methods. Here, the scalar e is represented in base 2^{w }using digits b_{i }from some digit set B:
A simple way to obtain a uniform sequence of doublings and additions (namely, one addition after w doublings in the main loop of the point multiplication algorithm) is to use 2^{w}ary point multiplication as usual (first compute and store bP for each bεB, then compute eP using this precomputed table), but to insert a dummy addition whenever a zero digit is encountered.
However, as noted above for the binary case, the dummy addition approach may not be secure.
This problem can be avoided (given w≧2) by using a representation of e without digit value 0, such as
B={−2^{w}, 1, 2, . . . , 2^{w}−1}
as proposed in [4], or
B={−2^{w}, ±1,±2, . . . , ±(2^{w}−2),2^{w}−1}
for improved efficiency as proposed in [12].
A remaining problem in the method of [4] and [12] is that the use of a fixed table may allow for statistical attacks: If the same point from the table is used in a point addition whenever the same digit value occurs, this may help adversaries to find out which of the digits b_{1}, have the same value (cf. the attacks on modular exponentiation using fixed tables in [13] and [14]).
This problem can be countered by performing, whenever the table is accessed, a projective randomization of the table value that has been used.
This will avoid a fixed table, but at the price of reduced efficiency.
This invention is a variant of 2^{w}ary point multiplication with resistance against sidechannel attacks that avoids a fixed table without requiring frequently repeated projective randomization.
An additional advantage of the new method is that it is easily parallelizable on twoprocessor systems. One essential change in strategy compared with earlier methods for sidechannel attack resistant point multiplication is the use of a righttoleft method (the scalar is processed starting at the least significant digit, cf. [15]) whereas the conventional methods work in a lefttoright fashion.
The method works in three stages, which are called initialization stage, righttoleft stage, and result stage.
First there will be a highlevel view of these stages before they are discussed in detail.
The method for computing eP is parameterized by an integer w≧2 and a digit set B consisting of 2^{w }integers of small absolute value such that every positive scalar e can be represented in the form
using digits b_{i}εB; for example
B={0, 1, . . . , 2^{w}−1}
or
B={−2^{w−1}, . . . , 2^{w−1}−1}
A representation of e using the latter digit set can be easily determined on the fly when scanning the binary digits of e in righttoleft direction.
If e is at most n bits long (i.e. 0<e<2^{n}), l=└n/w┘. is sufficient.
Let B′ denote the set {b∥bεB} of absolute values of digits, which has at least 2^{(w−1)}+1 and at most 2^{w }elements. The point multiplication method uses # (B)+1 variables for storing points on the elliptic curve in projective representation: Namely, one variable A_{b }for each bεB′, and one additional variable Q.
Let A_{b} ^{init }denote the value of A_{b }at the end of the initialization stage, and let A_{b} ^{sum }denote the value of A_{b }at the end of the righttoleft stage. The initialization stage sets up the variables A_{b}(bεB′) in a randomized way such that A_{b} ^{init}≠0 for each b, but
(O Denotes the Point at Infinity, the Neutral Element of the Elliptic Curve Group.)
Then the righttoleft stage performs computations depending on P and the digits b_{i}, yielding new values A_{b} ^{sum }of the variables A_{b }satisfying
for each bεB′. Finally, the result stage computes
which yields the final result eP because
The point multiplication method is a signeddigit variant of Yao's righttoleft method [15](see also [16, exercise 4.6.39]) and [17, exercise 4.6.39]) and [18]) with two essential modifications for achieving resistance against sidechannel attacks: The randomized initialization stage is different; and in the righttoleft stage, the digit 0 is treated like any other digit.
In the following the three stages are discussed in detail describing possible implementations.
The initialization stage can be implemented as follows:
and store it in variable A_{i}.
The resulting values of the variables A_{b }are denoted by A_{b} ^{init}.
If the elliptic curve is fixed, precomputation can be used to speed up the initialization stage:
The steps 1 and 2 should be run just once, e.g. during personalization of a smart card, and the resulting intermediate values A_{b }stored for future use.
These values are denoted by A_{b} ^{fix}. Then only step 3 (projective randomization of the values A_{b} ^{fix }to obtain new representations A_{b} ^{init}) has to be performed anew each time the initialization stage is called for. The points A_{b} ^{fix }must not be revealed; they should be protected like secret keys.
Generating a random point on an elliptic curve is straightforward. For each element X of the underlying field, there are zero, one or two values Y such that (X,Y) is the affine representation of a point on the elliptic curve.
Given a random candidate value X, it is possible to compute an appropriate Y if one exists; the probability for this is approximately ½ by Hasse's theorem.
If there is no appropriate Y, one can simply start again with a new X.
Computing an appropriate Y given X involves solving a quadratic equation, which usually (depending on the underlying field) is computationally expensive.
This makes it worthwhile to use precomputation as explained above.
It is also possible to reuse the values that have remained in the variables A_{b},b≠1, after a previous computation, and start at step 2 of the initialization stage.
To determine −
in step 2, it is not necessary to compute all the individual products bA_{b}.
The following Algorithm can be used instead to set up A_{1 }appropriately if B′={0, 1, . . . , β}, β≧2. (Note that both loops will be skipped in the case β=2.)


for i = β − 1 down to 2 do  
A_{i }← A_{i }+ A_{i+1}  
A_{1 }← 2A_{2}  
for i = 2 to β − 1do  
A_{i }← A_{i }− A_{l+1}  
A_{1 }← A_{1 }+ A_{l+1}  
A_{1 }← − A_{1}  
This algorithm takes one point doubling and 3β−6 point additions.
When it has finished, the variables A_{b }for 1<b<β will contain modified values, but these are representations of the points originally stored in the respective variables.
If sufficient memory is available, a faster algorithm can be used to compute A_{1 }without intermediate modification of the variables A_{b }for b>1 (use additional variables Q_{b }instead; a possible additional improvement can be achieved if point doublings are faster than point additions).
The projective randomization of the variables A_{b }(bεB′) in step 3 has the purpose to prevent adversaries from correlating observations from the computation of A_{1 }in the initialization stage with observations from the following righttoleft stage. If algorithm 1 has been used to compute A_{1 }and the points are not reused for multiple invocations of the initialization stage, then no explicit projective randomization of the variables A_{b }for 1<b<β is necessary; and if β>2 no explicit projective randomization of A_{1 }is necessary:
The variables have automatically been converted into new representations by the point additions used to determine their final values.
The following implements the righttoleft stage using a uniform pattern of point doublings and point additions.
Initially, for each b, variable A_{b }contains the value A_{b} ^{init}; the final value is denoted by A_{b} ^{sum}.
Algorithm 2 Righttoleft stage  
Q ← P  
for i = 0 to l do  
if b_{i }≧ 0 then  
A_{b} _{ i } ← A_{b} _{ i }+ Q  
else  
A_{b} _{ i } ← A_{b} _{ i } − Q  
Q ← 2^{w }Q  
Due to special cases that must be handled in the point addition algorithm ([19]), uniformity of this algorithm is violated if A_{b} _{ i } _{} is a projective representation of ±Q; the randomization in the initialization stage ensures that the probability of this is negligible.
(This is why in the section, where the initialization stage is described, it is required that precomputed values A_{b} ^{fix }be kept secret.)
If B contains no negative digits, the corresponding branch in the algorithm can be omitted.
The obvious way to implement Q←2^{w}Q in this algorithm is wfold iteration of the statement Q←2Q, but depending on the elliptic curve, more efficient specific algorithms for wfold point doubling may be available (see [20]).
In the final iteration of the loop, the assignment to Q may be skipped (the value Q is not used after the righttoleft stage has finished).
With this modification, the algorithm uses lw point doublings and l+1 point additions. Observe that on twoprocessor systems the point addition and the wfold point doubling in the body of the loop may be performed in parallel: Neither operations depends on the other's result.
Similarly to the computation of A_{1 }in the initialization stage, the result stage computation
can be performed without computing all the individual products bA_{b} ^{sum}. In the result stage, it is not necessary to preserve the original values of the variables A_{b}, so the following algorithm (from [16, answer to exercise 4.6.39]) can be used if B′={0, 1, . . . , β} when initially each variable A_{b }contains the value A_{b} ^{sum}.


for i = β − 1 down to 1 do  
A_{i }← A_{i }= A_{i+1}  
for i = 2 to β do  
A_{1 }← A_{1 }+ A_{i}  
return A_{1}  
This algorithm uses 2β−2 point additions. Elliptic curve point arithmetic usually has the property that point doublings are faster than point additions. Then the variant described in the following algorithm is advantageous.


for i = β down to 1 do  
if 2i ≦ β then  
A_{i }← A_{i }+ A_{2i}  
if i is even then  
if i < β then  
A_{i }← A_{i }+ A_{i+1}  
A_{i }← 2A_{i}  
else  
if i > l then  
A_{1 }← A_{1 }+ A_{i}  
return A_{l}  
This algorithm uses └β/2┘ point doublings and 2β−2−└β/2┘ point additions.
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U.S. Classification  380/30 
International Classification  G06F7/72, H04K1/00 
Cooperative Classification  G06F2207/7228, G06F7/725, G06F2207/7223 
European Classification  G06F7/72F1 
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